Introduction As we all know, along with the arrival of the era of big data, big data analysis has also gradually appeared, expanding, big data and mobile Internet era, every person using a mobile terminal all the time is not in the production of data, and as a product of Internet service provision, but also in the continuous accumulation of data. Data, like artificial intelligence, can often show a more objective and rational side, data can make people more intuitive and clear understanding of the world, data can also guide people to make more rational decisions. With the daily routine of big data, in order to prevent the flood of big data, so we must take data analysis in a timely manner, put forward useful data, that big data analysis of the common means of which there are several?

One, visualization analysis

Whether for data analysis experts or ordinary users, data visualization is the most basic requirement of data analysis tools. Visualization can intuitively show the data, let the data speak for themselves, so that the masses in a more intuitive, more understandable way to understand the results.

Two, data mining algorithms

Data mining, also known as knowledge discovery in databases artificial intelligence machine style, statistics, databases, visualization techniques, etc., a high degree of automation to analyze the enterprise's data, to make inductive reasoning, from which to dig out the potential patterns, to help decision makers to adjust the market strategy to reduce the risk of making the right decisions.

Saying then that visualization is the presentation of data in an intuitive form for people to see, data mining can be said to be for machines. Clustering, segmentation, isolated point analysis and other algorithms allow us to get inside the data and mine the value. These algorithms not only deal with the volume of big data, but also the speed at which it can be processed.

Three, predictive analytics capabilities

Predictive analytics combines a variety of advanced analytics capabilities, including ad hoc statistical analysis, predictive modeling, data mining, text analytics, optimization, real-time scoring, and machine learning. These tools help organizations discover patterns in their data and predict future progress beyond what is happening now.

Data mining allows analysts to understand data better, while predictive analytics allows analysts to make some predictive judgments based on the results of visual analytics and data mining.

Four, semantic engine

Since the diversity of unstructured data brings new challenges in data analysis, a series of tools are needed to parse, extract, and analyze the data. Semantic engines need to be designed to intelligently extract information from "documents".

V. Data quality and data management

Data quality and data management are some of the best practices in management. Working with data through standardized processes and tools ensures a pre-defined, high-quality analysis.

On the topic of "What are some common tools for big data analytics?" The content of the content is introduced to you here, more about big data analytics related content, pay attention to the editor, continue to update.